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What are stakeholders' perceptions on use of AI for health supply chains in Tanzania?

May 3rd, 2021
May 3rd, 2021

“To best serve the public health interest, AI and machine learning (ML) should be implemented via a thoughtful approach developed collaboratively with key stakeholders to ensure ownership, stewardship and sustainability.”- Harrison Mariki, Senior Advisor, inSupply Health

The Government of Tanzania (GoT) has recently made large investments in Health Information Systems (HIS) that aim to increase not only the quantity of data but also quality of health information and is now primed to solve health supply chain challenges with artificial intelligence (AI).

With new systems like the Vaccines Information Management System (VIMS) collecting high-frequency, high-quality data on logistics for vaccines, supplies, and performance of routine immunization activities, the GoT has created new opportunities to employ AI to improve the national health supply chains. AI uses algorithms to detect complex patterns in data to make predictions about the future and can quickly adapt to changes happening in real time within communities.

To kick start the conversation around AI for health supply chains in Tanzania, Macro-Eyes in partnership with inSupply Health, an independent supply chain advisory firm based in East Africa recently conducted a survey in Tanzania to understand what various stakeholders and AI practitioners thought of Tanzania’s readiness to implement AI solutions.

Why is Tanzania ready?

Over the past years, the GoT has invested much into creating technological infrastructure and a forward-looking culture and is now well-positioned to implement AI solutions. With collaboration and partnership the backbone of successful implementation of AI, the GoT’s strong track record of seeking out high-value partnerships, among other things, makes it ready. Over 60% of stakeholders agreed that Tanzania is ready to create more AI-centric partnerships, and 81% of stakeholders believe that the Tanzanian government will be able and willing to support AI initiatives.

In addition, Tanzania’s impressive investments in integrating and creating electronic data storage solutions have paid off: 59% of the stakeholders surveyed believe that Tanzania is ready to adapt Al due to availability of different systems such as Vaccine Information Management System (VIMS), the Tanzania Immunization Registry (TimR) , the electronic Logistic Management Information System (eLMIS) and the District Health Information System (DHIS 2), that provide high quality, high frequency data. Other electronic public data sources have also become more widely available, like population estimates from WorldPop.

How exactly AI can benefit Tanzania in the future?

Al has provided a positive impact in many sectors globally. For example, Google has used Al to increase energy and efficiency by giving data center cooling control to an AI and saved over 40% energy. The benefit is not limited to the private sector, many healthcare providers are using AI to help diagnose and treat cancer patients, including in Tanzania. Social programs are using AI to improve adherence to HIV treatment, boost graduation rates, and identify people eligible for government benefits. Amazon uses Al algorithms to better predict future demand trends as a way to reduce stock outs and help the company minimize losses and increase revenue. Large e-commerce companies including Amazon have taken Al a further step and combined AI with the use of robots to automate the process of product sorting and packing in the warehouse to increase efficiency and reduce error and therefore wastage (Puneete Bhalla, 2019).

Macro-Eyes has already shown that using TImR data and AI can improve the quality of vaccine supply chain forecasting. Our AI technology is able to accurately predict bi-weekly vaccine utilization for health facilities with a less than 3% error. With this evidence, survey respondents agreed that AI will have positive effects in Tanzania’s health supply chain as it will help reduce wastage, stock-outs and increase cost savings, efficiency in logistics and data triangulation.

Specifically, 92% of the stakeholders believe AI can help improve efficiency in logistics, 77% believe it will help in reducing stock-outs, 66% believe that it will save costs to a great extent as it can help minimize error and 63% believe it will help in reducing wastage.

What now? What needs to be done?

Despite the enthusiasm, the stakeholders still had concerns relating to data privacy, ethics and ownership, wariness of creating a proliferation of systems and tools, the absence of uniform standards to assess the impact of AI; and the potential loss of jobs. We believe that Tanzania AI stakeholders must work together to address these concerns.

For inSupply Health, meaningful digital inclusion is the end goal. To achieve this, inSupply Health together with Macro-Eyes, is currently taking on a collaborative and consultative approach to develop the Theory of Action (TOA) framework to guide integration, expansion and sustainability in AI technology in the health supply chains. This framework will guide future implementation of AI in Tanzanian health supply chains and ensure AI is adopted sustainably and responsibly.

Stakeholders agree that implementing AI in Tanzania will benefit the health supply chain.

*Authors

This article was co-authored by Jacqueline Minja, Supply Chain Analyst at inSupply Health and Johnna Sundberg, Machine Learning Scientist at Macro-eyes *